The Role of Emerging Technologies in Managed Transportation Services

When I ask shippers what they look for in a logistics or transportation management partner, technology capabilities — and the data and insights those technologies provide — is always near the top of the list. Of course, the technology landscape is very different today compared to 5-10 years ago, with blockchain, machine learning, real-time freight visibility, and other emerging technologies dominating the headlines. What impact are these technologies having on transportation managed services? How are they delivering value to companies? How will these trends continue to evolve in 2019 and beyond? Those are some of the questions I discussed with Steve Barber, Vice President-IT Customer Solutions at Transplace, in a recent episode of Talking Logistics.

Convergence of technology and managed services

Several years ago, I wrote about how the worlds of technology, managed services and consulting were converging, particularly in transportation management. Therefore, I began my discussion with Steve by asking him if this convergence is still happening and why.

Steve notes that convergence continues to occur largely due to the “blue dot” effect where people [are the blue dots on a map] and they know exactly where they are and what’s around them and they want things delivered directly to them. In their personal lives, people have come to expect complete visibility to their orders and deliveries and they expect the same visibility in their business lives too. “And it’s not only visibility to shipments, but unlimited selection and lowest cost options, as well,” says Steve.

The role of technology

Diving deeper into the role of technology, I asked Steve about the current state of visibility solutions. He notes that trucks have been equipped with GPS-enabled devices for over 30 years, but new solutions are making real-time freight visibility more broadly available. “But just knowing where your shipment is right now is no longer enough,” says Steve. “You also want to know how far it has to go to get here; its estimated time of arrival; and its dock appointment time; then link that information to my other systems.”

Steve compares this process to manufacturing where just knowing [via sensors and IoT] that a machine is running at X rpm doesn’t help much. You also need to know what that means for operations. “The goal is to make real-time decisions,” explains Steve. “That’s where advancements in machine learning and artificial intelligence are helpful. Companies can use this data beyond where a truck is to make useful decisions.

“The big challenge is to incorporate the needs of carriers and shippers,” continues Steve. “Knowing when trucks will be available can change how they bid loads and manage their ecosystem.”

Machine Learning

Since Steve mentioned machine learning, I asked him how much of that is hype versus reality. Steve commented that for many years logistics and transportation professionals heard all the cool technology hype in other industries while they still were sending EDI transactions back and forth to book freight. “But in 2017 and 2018 that began to change with the emergence of machine learning. Data availability is at an all-time high, which is needed to feed machine learning. Transaction volume is high to enable useful analysis. And the technology is available in the cloud or on-site. All three are necessary to make machine learning work. The challenge right now is finding the right use cases, such as continuous moves or predictive analytics and service.”

Build vs. buy

I asked Steve about the age-old build vs. buy decision as it relates to these new technologies. He points out several things to consider.

“You have to look at where you are compared to where you want to go. Do you have the infrastructure in place? Do you have the right IT skills and data scientists these new applications require? Data scientists are in high demand but short supply right now. Will it require a new platform such as an ERP, TMS or WMS? Also, you have to understand what the end use case is. There is a tendency to want to ‘boil the ocean’ with the first project. It’s better to start with a small project and learn from that and then decide where you want to go from there. You also need to look at the change management aspects and manage perceptions vs. reality.”

Blockchain, machine learning and future technologies

You can’t have a technology discussion today without bringing up blockchain. So I asked Steve for his thoughts on blockchain and how it, machine learning and other new technologies will impact managed services in 2019 and beyond. I encourage you to watch the full episode for his insights on this topic and more. Then post a comment and share your perspective!